SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples (2201.05979v3)

Abstract: Unsupervised sentence embedding aims to obtain the most appropriate embedding for a sentence to reflect its semantic. Contrastive learning has been attracting developing attention. For a sentence, current models utilize diverse data augmentation methods to generate positive samples, while consider other independent sentences as negative samples. Then they adopt InfoNCE loss to pull the embeddings of positive pairs gathered, and push those of negative pairs scattered. Although these models have made great progress on sentence embedding, we argue that they may suffer from feature suppression. The models fail to distinguish and decouple textual similarity and semantic similarity. And they may overestimate the semantic similarity of any pairs with similar textual regardless of the actual semantic difference between them. This is because positive pairs in unsupervised contrastive learning come with similar and even the same textual through data augmentation. To alleviate feature suppression, we propose contrastive learning for unsupervised sentence embedding with soft negative samples (SNCSE). Soft negative samples share highly similar textual but have surely and apparently different semantic with the original samples. Specifically, we take the negation of original sentences as soft negative samples, and propose Bidirectional Margin Loss (BML) to introduce them into traditional contrastive learning framework, which merely involves positive and negative samples. Our experimental results show that SNCSE can obtain state-of-the-art performance on semantic textual similarity (STS) task with average Spearman's correlation coefficient of 78.97% on BERTbase and 79.23% on RoBERTabase. Besides, we adopt rank-based error analysis method to detect the weakness of SNCSE for future study.

SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples

The paper "SNCSE: Contrastive Learning for Unsupervised Sentence Embedding with Soft Negative Samples" presents a novel approach to sentence embedding where the semantic representation of sentences is improved through contrastive learning techniques, specifically by integrating soft negative samples. The authors address a critical issue in unsupervised sentence embedding—the challenge of distinguishing and decoupling textual similarity from semantic similarity—by introducing a method that employs soft negative samples that share textual similarity but differ semantically.

Key Contributions

This research introduces the SNCSE framework, which utilizes soft negative samples generated through sentence negation to mitigate feature suppression inherent in current unsupervised sentence embedding models. The framework enhances the traditional contrastive learning paradigm, which typically handles only positive and negative samples, by integrating the concept of soft negatives. This integration is managed with a novel loss function, Bidirectional Margin Loss (BML), that adjusts for the semantic differences between positive and soft negative pairs.

Methodology

The SNCSE framework applies contrastive learning with the InfoNCE loss for positive and negative samples and employs BML to model the semantic differences between original sentences and their negated forms as soft negative samples. Utilizing pretrained LLMs such as BERT and RoBERTa, the paper demonstrates the methodology for encoding these samples with specific prompts, highlighting the efficiency of SNCSE in recognizing semantic nuances through feature suppression mitigation.

Results and Analysis

The experimental results indicate that the SNCSE framework achieves competitive performance in the Semantic Textual Similarity (STS) task, setting new benchmarks with Spearman's correlation coefficients of 78.97% and 79.23% for BERT and RoBERTa base models, respectively. These results illustrate the capability of SNCSE to grasp semantic content more effectively than previous approaches, especially regarding sentences sharing similar textual structures but differing in semantic meaning.

Through ablation studies, the paper clearly demonstrates the superiority of modeling negations as soft negatives rather than purely positive or negative samples, allowing the system to better differentiate semantic nuances. Additionally, rank-based error analysis reveals areas where feature suppression remains a challenge, notably when dealing with negation logic, word order, typos, and textual independency, emphasizing directions for future research.

Implications

The practical implications of this research are significant for NLP applications requiring nuanced semantic comprehension, such as machine translation, sentiment analysis, and textual entailment. Theoretically, this work advances the understanding of how to effectively use contrastive learning to enhance sentence embeddings by addressing feature suppression.

Conclusion

The paper makes a substantial contribution to the field of sentence embeddings by proposing methods that more accurately model semantic differences without the need for labeled data. While the SNCSE framework shows promise in addressing feature suppression, there remains ongoing work to further refine these models and overcome existing challenges. Future explorations may involve strengthening encoder architectures or introducing more sophisticated contrastive learning protocols to further mitigate feature suppression and improve semantic accuracy.